Sensitivity Analysis of Hydrological Modeling in the WRF-Urban Modeling System using Advanced Monte Carlo Simulations

Rapid urbanization has emerged as the source of many adverse effects that challenge the environmental sustainability of cities under changing climatic patterns. One essential key to address these challenges is to physically resolve the dynamics of urban-land-atmospheric interactions. Towards this objective, recently a physically-based urban hydrological model has been embedded into the framework of the single layer urban canopy model (SLUCM) of the Weather Research and Forecasting (WRF) platform. Though the coupled WRF/SLUCM has been extensively tested against various field measurement datasets, accurate input parameter space needs to be specified for good model performance. As realistic measurements of all input parameters to the modeling framework are rarely possible, understanding the model sensitivity to individual parameters is essential to determine the relative importance of parameter uncertainty to model performance. In this study, we use an advanced Monte Carlo approach to quantify relative sensitivity of input parameters of the hydrological model. In particular, performance of two widely used soil hydraulic models, namely the van Genuchten model (based on generic soil physics) and an empirical model (viz. the CHC model currently adopted in WRF/SLUCM) is investigated. Results show that the CHC model requires a much finer time step for numerical stability in hydrological modeling and thus is more computationally expensive in the coupled WRF/SLUCM modeling environment. In addition, numerical simulations are extended to evaluate the performance of different designs of green roofs as an effective mitigation strategy to the well-known urban heat island effect.